PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track4. Machine Learning in Education
Methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs
A.V. Ilina*, S. Belov, I. Filozova, Y. Gavrilenko, J. Javadzade, I. Kadochnikov, V. Korenkov, I. Pelevanyuk, D. Priakhina, R. Semenov, V. Tarabrin and P. Zrelov
Full text: pdf
Pre-published on: November 17, 2022
Published on:
Abstract
The article discusses the methods and algorithms that underlie the analytical platform for automated monitoring and analysis of the labor market in the Russian Federation, as well as the analysis of the higher education system's compliance with the labor market's current needs. The study involved natural language processing methods and Big Data technologies. The general scheme corresponds to end-to-end processing – from data collection and storage, their transformation, analysis, and modeling, to visualization of results and decision-making. The analytical core of the system is a module for intellectual analysis of the texts of job advertisements in the labor market. The vacancies are collected from the most complete databases in Russia (namely HeadHunter, Work in Russia and SuperJob). Job descriptions of vacancies are matched with the official list of professions of the Ministry of Labor and Social Protection of Russia using semantic analysis based on neural models trained on large arrays of texts. Also, using semantic analysis, automated monitoring and intellectual analysis of the staffing needs of the all-Russian and regional labor markets are carried out according to the range of specialties of the university. Data gathering has been ongoing from 2015 up to now.
DOI: https://doi.org/10.22323/1.429.0028
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.